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Articoli di riviste sul tema "DIT prediction"

Autore: Grafiati

Pubblicato: 7 giugno 2025

Ultima modifica: 2 agosto 2025

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1

Prykhodko, Sergiy, Natalia Prykhodko, and Tetyana Smykodub. "A Statistical Evaluation of The Depth of Inheritance Tree Metric for Open-Source Applications Developed in Java." Foundations of Computing and Decision Sciences 46, no. 2 (2021): 159–72. http://dx.doi.org/10.2478/fcds-2021-0011.

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Abstract The Depth of Inheritance Tree (DIT) metric, along with other ones, is used for estimating some quality indicators of software systems, including open-source applications (apps). In cases involving multiple inheritances, at a class level, the DIT metric is the maximum length from the node to the root of the tree. At an application (app) level, this metric defines the corresponding average length per class. It is known, at a class level, a DIT value between 2 and 5 is good. At an app level, similar recommended values for the DIT metric are not known. To find the recommended values for t

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Lin, Jinzhu, Yujie He, Chengxiang Ru, Wulin Long, Menglong Li, and Zhining Wen. "Advancing Adverse Drug Reaction Prediction with Deep Chemical Language Model for Drug Safety Evaluation." International Journal of Molecular Sciences 25, no. 8 (2024): 4516. http://dx.doi.org/10.3390/ijms25084516.

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The accurate prediction of adverse drug reactions (ADRs) is essential for comprehensive drug safety evaluation. Pre-trained deep chemical language models have emerged as powerful tools capable of automatically learning molecular structural features from large-scale datasets, showing promising capabilities for the downstream prediction of molecular properties. However, the performance of pre-trained chemical language models in predicting ADRs, especially idiosyncratic ADRs induced by marketed drugs, remains largely unexplored. In this study, we propose MoLFormer-XL, a pre-trained model for enco

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Chen, Weijun, and Yanze Wang. "DHMoE: Diffusion Generated Hierarchical Multi-Granular Expertise for Stock Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 11 (2025): 11490–99. https://doi.org/10.1609/aaai.v39i11.33250.

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Stock prediction stands as a pivotal research objective within the Fintech. Existing deep learning research revolves around the development and scaling of one individual neural network predictor. However, in the dynamic and noisy landscape of the stock market, reliance solely on a single predictor poses risks of limited adaptability to diverse market conditions and challenges in effectively integrating multi-source information. Besides, top-down teaching and bottom-up hierarchical decision-making paradigms are critical for robust and accurate stock prediction within successful quantitative fir

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Lv, Lingling, Pucheng Pei, Peng Ren, He Wang, and Geng Wang. "Exploring Performance Degradation of Proton Exchange Membrane Fuel Cells Based on Diffusion Transformer Model." Energies 18, no. 5 (2025): 1191. https://doi.org/10.3390/en18051191.

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Proton exchange membrane fuel cells (PEMFCs) stand at the forefront of energy conversion technology, efficiently converting the chemical energy of hydrogen and oxygen directly into electricity. Research on predicting the remaining useful life of PEMFCs has long been a focus, as it plays a crucial role in preventing failures and mitigating safety risks. This paper introduces a robust diffusion transformer (DiT) model, which is a novel approach leveraging generative artificial intelligence (GAI) technology to innovate the existing methods for predicting the performance degradation of PEMFCs. Thi

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Prykhodko, A. S., and E. V. Malakhov. "DETERMINING OBJECT-ORIENTED DESIGN COMPLEXITY DUE TO THE IDENTIFICATION OF CLASSES OF OPEN-SOURCE WEB APPLICATIONS CREATED USING PHP FRAMEWORKS." Radio Electronics, Computer Science, Control, no. 2 (June 27, 2024): 160. http://dx.doi.org/10.15588/1607-3274-2024-2-16.

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Context. The problem of determining the object-oriented design (OOD) complexity of the open-source software, including Web apps created using the PHP frameworks, is important because nowadays open-source software is growing in popularity and using the PHP frameworks making app development faster. The object of the study is the process of determining the OOD complexity of the open-source Web apps created using the PHP frameworks. The subject of the study is the mathematical models to determine the OOD complexity due to the identification of classes of the open-source Web apps created using the

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Kar, Subhajit, Madhabi Ganguly, and Saptarshi Das. "USING DIT-FFT ALGORITHM FOR IDENTIFICATION OF PROTEIN CODING REGION IN EUKARYOTIC GENE." Biomedical Engineering: Applications, Basis and Communications 31, no. 01 (2019): 1950002. http://dx.doi.org/10.4015/s1016237219500029.

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The new research platform on biomedical engineering by Digital Signal Processing (DSP) is playing a vital role in the prediction of protein coding regions (Exons) from genomic sequences with great accuracy. We can determine the protein coding area in DNA sequences with the help of period-3 property. It has been seen that in order to find out the period-3 property, the DFT algorithm is mostly used but in this paper, we have tested FFT algorithm instead of DFT algorithm. DSP is basically concerned with processing numerical sequences. When digital signal processing used in DNA sequences analysis,

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Yousefi, Mehran, Mehdi Panahali, Kamran Azarkhish, et al. "Diagnostic value of DWI-MRI for the detection of acute plaques in the relapse phase of multiple sclerosis." Romanian Journal of Neurology 20, no. 1 (2021): 35–40. http://dx.doi.org/10.37897/rjn.2021.1.5.

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Background. The 2010 revision of the McDonald criteria, widely used for the diagnosis of multiple sclerosis (MS), has established that dissemination in time (DIT) can be demonstrated by the simultaneous presence of asymptomatic gadolinium-enhancing and non-enhancing plaques on a single magnetic resonance imaging (MRI). When the use of gadolinium contrast agents is contraindicated, diffusion-weighted imaging (DWI) is utilized to confirm diffusion alterations in active inflammatory plaques. This study intended to examine whether DWI can be a viable alternative to contrast-enhanced T1-weighted im

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Aziz, Syed Rashid, Tamim Ahmed Khan, and Aamer Nadeem. "Exclusive use and evaluation of inheritance metrics viability in software fault prediction—an experimental study." PeerJ Computer Science 7 (June 4, 2021): e563. http://dx.doi.org/10.7717/peerj-cs.563.

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Software Fault Prediction (SFP) assists in the identification of faulty classes, and software metrics provide us with a mechanism for this purpose. Besides others, metrics addressing inheritance in Object-Oriented (OO) are important as these measure depth, hierarchy, width, and overriding complexity of the software. In this paper, we evaluated the exclusive use, and viability of inheritance metrics in SFP through experiments. We perform a survey of inheritance metrics whose data sets are publicly available, and collected about 40 data sets having inheritance metrics. We cleaned, and filtered t

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Taylor, C. S., J. I. Murray, and A. W. Illius. "Relative growth of incisor arcade breadth and eating rate in cattle and sheep." Animal Science 45, no. 3 (1987): 453–58. http://dx.doi.org/10.1017/s0003356100002932.

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ABSTRACTMaximum eating rate, rmax in kj metabolizable energy per min, at a given body weight W kg, can be predicted in normally growing cattle when adult body weight, A kg, is known, by the formula of Taylor and Murray (1987) as rmax = 31μ0·86 A0·73 where u = W/A is degree of maturity in body weight. When the pattern of normal growth is disturbed by fluctuating levels of food intake, a better prediction can be obtained in terms of incisor arcade breadth. This paper gives the allometric relationship between degree of maturity in body weight, u, and degree of maturity in incisor arcade breadth u

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Lebedevas, Sergejus, Saugirdas Pukalskas, and Vygintas Daukšys. "MATHEMATICAL MODELLING OF INDICATIVE PROCESS PARAMETERS OF DUAL-FUEL ENGINES WITH CONVENTIONAL FUEL INJECTION SYSTEM." Transport 35, no. 1 (2020): 57–67. http://dx.doi.org/10.3846/transport.2020.12212.

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Modern engine research uses multi-dimensional Mathematical Models (MMs) that are applicable to multi-fuel engines. However, their use involves the availability of detailed technical data on the design and characteristics of the engine, which is not always possible. The use of a one-dimensional MM is more expedient for the prediction of engine parameters, but their application for this purpose has not yet been sufficiently investigated. This publication presents the results of numerical studies evaluating the application of a one-dimensional MM with bi-phase Vibe combustion laws for dual-fuel (

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Dabou, Raoult Teukam, Innocent Kamwa, Jacques Tagoudjeu, and Francis Chuma Mugombozi. "Sparse Signal Reconstruction on Fixed and Adaptive Supervised Dictionary Learning for Transient Stability Assessment." Energies 14, no. 23 (2021): 7995. http://dx.doi.org/10.3390/en14237995.

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Fixed and adaptive supervised dictionary learning (SDL) is proposed in this paper for wide-area stability assessment. Single and hybrid fixed structures are developed based on impulse dictionary (ID), discrete Haar transform (DHT), discrete cosine transform (DCT), discrete sine transform (DST), and discrete wavelet transform (DWT) for sparse features extraction and online transient stability prediction. The fixed structures performance is compared with that obtained from transient K-singular value decomposition (TK-SVD) implemented while adding a stability status term to the optimization probl

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ПРИХОДЬКО, СЕРГІЙ, та ІВАН ШУТКО. "РАННЄ ОЦІНЮВАННЯ КІЛЬКОСТІ РЯДКІВ КОДУ ВЕБ-ЗАСТОСУНКІВ, ЩО СТВОРЮЮТЬСЯ ЗА ДОПОМОГОЮ PHP ФРЕЙМВОРКІВ". Herald of Khmelnytskyi National University. Technical sciences 349, № 2 (2025): 487–92. https://doi.org/10.31891/2307-5732-2025-349-71.

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The problem of early estimation of lines of code count in software projects holds significant importance, as it directly influences the prediction of software development effort, including web applications created using the well-known PHP frameworks, as the CakePHP and Codeigniter. The object of the study is the process of early estimating the lines of code count of web applications created using the CakePHP and Codeigniter frameworks. The subject of the study is the regression models for early estimating the lines of code count of web applications created using the CakePHP and Codeigniter fra

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Andrasto, T., Musaropah, Haryono, T. Joko, and Kardoyo. "Simulation and design of smart clothesline using fuzzy for weather forecast." IOP Conference Series: Earth and Environmental Science 969, no. 1 (2022): 012058. http://dx.doi.org/10.1088/1755-1315/969/1/012058.

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Abstract Information about weather is very important for human life. For this reason, a weather prediction system is needed that can read predictions correctly. One of the correct prediction systems is fuzzy systems. Fuzzy systems are used because they can make accurate and accurate weather predictions like human logic. The system used needs to be simulated to obtain the right model. The right software to simulate is Simulink MATLAB. In this study will take the DHT 22 and LDR (Light Dependent Resistor) sensor data from Arduino which will be processed by Simulink MATLAB using the Fuzzy Mamdani

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Prykhodko, Sergiy, Ivan Shutko, and Andrii Prykhodko. "Early size estimation of web apps created using codeigniter framework by nonlinear regression models." RADIOELECTRONIC AND COMPUTER SYSTEMS, no. 3 (October 4, 2022): 84–94. http://dx.doi.org/10.32620/reks.2022.3.06.

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Subject matter: Early software size estimation is one of the project managers' significant problems in evaluating app development efforts because software size is the major determinant of software project effort. Function points (FPs) and lines of code (LOC) are most commonly used as measures of size in existing software effort estimation methods and models. As is known, both these metrics have their advantages and disadvantages when used for software effort estimation. Although the FPs-based measure has the advantage over the LOC in that it does not depend on the technologies used, however, t

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Melo, Carolina Pereira de Souza, Catharina Brant Campos, Alvaro Pimenta Dutra, et al. "Gene Expression Profiling in the Classification of Acute Leukemia Brazilian Patients." Blood 124, no. 21 (2014): 5315. http://dx.doi.org/10.1182/blood.v124.21.5315.5315.

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Abstract In the past few decades, genetic data has become increasingly important for acute leukemia diagnosis and patients stratification. Indeed, the present World Health Organization (WHO) leukemia classification system is largely based upon genetically defined subgroups. Gene expression profile (GEP) may correctly predict most genetic leukemia subtypes, but so far no GEP report has evaluate patients from Latin America. In the present study, we used gene expression microarray data to build an acute leukemia classifier. Bone marrow samples were collected from 231 individuals at diagnosis, 110

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16

Broo, Anders, and Sten O. Nilsson Lill. "Transferable force field for crystal structure predictions, investigation of performance and exploration of different rescoring strategies using DFT-D methods." Acta Crystallographica Section B Structural Science, Crystal Engineering and Materials 72, no. 4 (2016): 460–76. http://dx.doi.org/10.1107/s2052520616006831.

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A new force field, here called AZ-FF, aimed at being used for crystal structure predictions, has been developed. The force field is transferable to a new type of chemistry without additional training or modifications. This makes the force field very useful in the prediction of crystal structures of new drug molecules since the time-consuming step of developing a new force field for each new molecule is circumvented. The accuracy of the force field was tested on a set of 40 drug-like molecules and found to be very good where observed crystal structures are found at the top of the ranked list of

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Uttam, Kumar* Priyal jain Dr. Jitendra banweer. "Artificial Intelligence Based Drug Designing." International Journal of Pharmaceutical Sciences 3, no. 5 (2025): 1535–52. https://doi.org/10.5281/zenodo.15380466.

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The combination of Artificial Intelligence (AI) and pharmaceutical science is creating exciting changes in the way new medicines are discovered and developed. Significant developments in artificial intelligence and machine learning offer a game-changing prospect for pharmaceutical dosage form testing, formulation, and medication discovery. AI can help lower development costs. In addition to predicting the pharmacokinetics and toxicity of potential drugs, machine learning techniques aid in the design of experiment. By prioritizing and optimizing lead compounds, this capability lessens the need

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Zhang, Danchen, and Daqing He. "Enhancing Clinical Decision Support Systems with Public Knowledge Bases." Data and Information Management 1, no. 1 (2017): 49–60. http://dx.doi.org/10.1515/dim-2017-0005.

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Abstract With vast amount of biomedical literature available online, doctors have the benefits of consulting the literature before making clinical decisions, but they are facing the daunting task of finding needles in haystacks. In this situation, it would be of great use to the doctors if an effective clinical decision support system is available to generate accurate queries and return a manageable size of highly useful articles. Existing studies showed the usefulness of patients’ diagnosis information in supporting effective retrieval of relevant literature, but such diagnosis information is

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Konvička, Martin. "Structurele toekomst, predicties, natijdigheid en emergente grammatica: Een bijdrage tot de discussie over panchronie." Roczniki Humanistyczne 64, no. 5 (2017): 63–82. https://doi.org/10.18290/rh.2016.64.5s-5.

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In dit artikel onderzoek ik twee theoretische concepten met betrekking tot de aard van grammaticale structuren. Ten eerste bespreek ik het begrip panchronie als een niet-reductief alternatief voor de structurele dichotomie van diachronie versus synchronie. Ten tweede introduceer ik een emergentistische benadering van taal die de idee van taal als een statisch, stabiel systeem verwerpt. Daarna laat ik zien hoe een panchronische taalvisie de emergentistische benadering ten goede komt. Hierbij zal ik het belang van structurele posterioriteit benadrukken die in traditionele grammaticamodellen vaak

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Wang, Zhengyu, Chengyi Tu, Jingjing Fan, et al. "Caragana microphylla (Shrub) Seedlings Exhibit Better Growth than Surrounding Herbs Under Drought Conditions." Sustainability 17, no. 3 (2025): 1142. https://doi.org/10.3390/su17031142.

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Shrub encroachment is a global ecological issue. The changes in growth dynamics between shrub seedlings and herbs are pivotal in determining shrub encroachment, yet their response to varying rainfall regimes remains unclear. We conducted a precipitation manipulation experiment (three precipitation (P) amount treatments: P−25% (225 mm), P (300 mm), P+25% (375 mm); three drought interval treatments: DI4, DI6, DI8) on a mixture of Caragana microphylla (shrub) seedlings and four herbs (Neotrinia splendens, Campeiostachys dahurica, Lolium multiflorum and Medicago sativa), analyzing their ecophysiol

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Kim, Sung-Hun, Il-Ju Moon, and Pao-Shin Chu. "Statistical–Dynamical Typhoon Intensity Predictions in the Western North Pacific Using Track Pattern Clustering and Ocean Coupling Predictors." Weather and Forecasting 33, no. 1 (2018): 347–65. http://dx.doi.org/10.1175/waf-d-17-0082.1.

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Abstract A statistical–dynamical model for predicting tropical cyclone (TC) intensity has been developed using a track-pattern clustering (TPC) method and ocean-coupled potential predictors. Based on the fuzzy c-means clustering method, TC tracks during 2004–12 in the western North Pacific were categorized into five clusters, and their unique characteristics were investigated. The predictive model uses multiple linear regressions, where the predictand or the dependent variable is the change in maximum wind speed relative to the initial time. To consider TC-ocean coupling effects due to TC-indu

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TOWSEY, MICHAEL W., JAMES J. GORDON, and JAMES M. HOGAN. "THE PREDICTION OF BACTERIAL TRANSCRIPTION START SITES USING SVMS." International Journal of Neural Systems 16, no. 05 (2006): 363–70. http://dx.doi.org/10.1142/s0129065706000767.

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Identifying promoters is the key to understanding gene expression in bacteria. Promoters lie in tightly constrained positions relative to the transcription start site (TSS). In this paper, we address the problem of predicting transcription start sites in Escherichia coli. Knowing the TSS position, one can then predict the promoter position to within a few base pairs, and vice versa. The accepted method for promoter prediction is to use a pair of position weight matrices (PWMs), which define conserved motifs at the sigma-factor binding site. However this method is known to result in a large num

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Diego, KY M., D. B. Putungan, and A. B. Santos-Putungan. "Predicting the minimum energy pathway of 1H to 1T phase transition of select 2D transition metal dichalcogenides via density functional theory and machine learning approach." Journal of Physics: Conference Series 2793, no. 1 (2024): 012017. http://dx.doi.org/10.1088/1742-6596/2793/1/012017.

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Abstract This study predicts the minimum energy pathways (MEPs) and transition barriers of two-dimensional transition metal dichalcogenides (TMDs) during their 1H to 1T structural phase transitions. The investigation utilizes density functional theory (DFT) calculations and machine learning algorithms to predict the MEPs and transition barriers. Six TMDs, namely NbSe2, ScS2, ScSe2, TiTe2, VS2, and VSe2, are selected for analysis. The DFT calculations provide reference values for comparison with the machine learning predictions. The transition barriers obtained through DFT calculations range fr

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Wang, Zongguo, Ziyi Chen, Yang Yuan, and Yangang Wang. "CrySPAI: A New Crystal Structure Prediction Software Based on Artificial Intelligence." Inventions 10, no. 2 (2025): 26. https://doi.org/10.3390/inventions10020026.

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Crystal structure predictions based on the combination of first-principles calculations and machine learning have achieved significant success in materials science. However, most of these approaches are limited to predicting specific systems, which hinders their application to unknown or unexplored domains. In this paper, we present a crystal structure prediction software based on artificial intelligence, named as CrySPAI, to predict energetically stable crystal structures of inorganic materials given their chemical compositions. The software consists of three key modules, an evolutionary opti

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Maguire, Douglas A., and David W. Hann. "Bark Thickness and Bark Volume in Southwestern Oregon Douglas-Fir." Western Journal of Applied Forestry 5, no. 1 (1990): 5–8. http://dx.doi.org/10.1093/wjaf/5.1.5.

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Abstract A segmented polynomial taper equation for southwestern Oregon Douglas-fir (Pseudotsuga menziesii) predicts double bark thickness (dbt) at any point above breast height. Below breast height predictions assume conformity to a neiloid frustrum. The equations facilitate estimation of inside bark diameter (dib) given outside bark (dob) measurements. Bark volume and bark biomass can also be estimated when supplemented with existing dib taper equations developed for southwestern Oregon. West J. Appl. For. 5(1):5-8.

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Weerasekera, Naveen, Siyua Cao, and Laksman Perera. "Functional Property Evaluation of Crystalline Materials using Density Functional Theory: A Review." European Journal of Applied Physics 4, no. 1 (2022): 19–26. http://dx.doi.org/10.24018/ejphysics.2022.4.1.142.

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In this paper, utilization of density functional theory (DFT) to obtain mechanical, electrical and thermal properties of crystalline materials are reviewed. DFT has resulted as an efficient tool for predicting ground states of many body systems thus aiding in resolving dispersion spectrums of complex atomic arrangements where solution by traditional Schr dinger (SH) equation is infeasible. Great success has been reported by previous researchers on utilizing DFT for functional property predictions of crystalline solids.

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Naveen, Weerasekera, Cao Siyua, and Perera Laksman. "Functional Property Evaluation of Crystalline Materials using Density Functional Theory: A Review." European Journal of Applied Physics 4, no. 1 (2022): 19–26. https://doi.org/10.24018/ejphysics.2022.4.1.142.

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In this paper, utilization of density functional theory (DFT) to obtain mechanical, electrical and thermal properties of crystalline materials are reviewed. DFT has resulted as an efficient tool for predicting ground states of many body systems thus aiding in resolving dispersion spectrums of complex atomic arrangements where solution by traditional Schr𝒐̇dinger (SH) equation is infeasible. Great success has been reported by previous researchers on utilizing DFT for functional property predictions of crystalline solids

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He, Guoqiang, Qingzu He, Jinyan Cheng, Rongwen Yu, Jianwei Shuai, and Yi Cao. "ProPept-MT: A Multi-Task Learning Model for Peptide Feature Prediction." International Journal of Molecular Sciences 25, no. 13 (2024): 7237. http://dx.doi.org/10.3390/ijms25137237.

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In the realm of quantitative proteomics, data-independent acquisition (DIA) has emerged as a promising approach, offering enhanced reproducibility and quantitative accuracy compared to traditional data-dependent acquisition (DDA) methods. However, the analysis of DIA data is currently hindered by its reliance on project-specific spectral libraries derived from DDA analyses, which not only limits proteome coverage but also proves to be a time-intensive process. To overcome these challenges, we propose ProPept-MT, a novel deep learning-based multi-task prediction model designed to accurately for

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Tong, Yubing, Jayaram K. Udupa, Emeline Chong, et al. "Prediction of lymphoma response to CAR T cells by deep learning-based image analysis." PLOS ONE 18, no. 7 (2023): e0282573. http://dx.doi.org/10.1371/journal.pone.0282573.

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Clinical prognostic scoring systems have limited utility for predicting treatment outcomes in lymphomas. We therefore tested the feasibility of a deep-learning (DL)-based image analysis methodology on pre-treatment diagnostic computed tomography (dCT), low-dose CT (lCT), and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) images and rule-based reasoning to predict treatment response to chimeric antigen receptor (CAR) T-cell therapy in B-cell lymphomas. Pre-treatment images of 770 lymph node lesions from 39 adult patients with B-cell lymphomas treated with CD19-directed CAR T-cell

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Yang, Zong-Chang. "Electric Load Movement Forecasting Based on the DFT Interpolation with Periodic Extension." Journal of Circuits, Systems and Computers 24, no. 08 (2015): 1550123. http://dx.doi.org/10.1142/s0218126615501236.

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Electric load forecasting is increasingly important for the industry. This study addresses the load forecasting based on the discrete Fourier transform (DFT) interpolation. As the most common analysis method in the frequency domain, the conventional Fourier analysis cannot be directly applied to prediction. From the perspective of time-series analysis, electric load movement influenced by various factors is also a time-series, which is usually subject to cyclical variations. Then with periodic extension for the load movement, a forecasting approach based on the DFT interpolation is proposed fo

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Harper, Angela F., Matthew L. Evans, James P. Darby, et al. "Ab initio Structure Prediction Methods for Battery Materials : A review of recent computational efforts to predict the atomic level structure and bonding in materials for rechargeable batteries." Johnson Matthey Technology Review 64, no. 2 (2020): 103–18. http://dx.doi.org/10.1595/205651320x15742491027978.

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Portable electronic devices, electric vehicles and stationary energy storage applications, which encourage carbon-neutral energy alternatives, are driving demand for batteries that have concurrently higher energy densities, faster charging rates, safer operation and lower prices. These demands can no longer be met by incrementally improving existing technologies but require the discovery of new materials with exceptional properties. Experimental materials discovery is both expensive and time consuming: before the efficacy of a new battery material can be assessed, its synthesis and stability m

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Xie, Li, Zhengming Chen, and Sheng Yu. "Deep Convolutional Transformer Network for Stock Movement Prediction." Electronics 13, no. 21 (2024): 4225. http://dx.doi.org/10.3390/electronics13214225.

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The prediction and modeling of stock price movements have been shown to possess considerable economic significance within the finance sector. Recently, a range of artificial intelligence methodologies, encompassing both traditional machine learning and deep learning approaches, have been introduced for the purpose of forecasting stock price fluctuations, yielding numerous successful outcomes. Nonetheless, the identification of effective features for predicting stock movements is considered a complex challenge, primarily due to the non-linear characteristics, volatility, and inherent noise pres

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Takeuchi, Masashi, Hideaki Suzuki, Yasuharu Matsumoto, et al. "Prediction of the development of delirium after transcatheter aortic valve implantation using preoperative brain perfusion SPECT." PLOS ONE 17, no. 11 (2022): e0276447. http://dx.doi.org/10.1371/journal.pone.0276447.

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Objectives Delirium is an important prognostic factor in postoperative patients undergoing cardiovascular surgery and intervention, including transcatheter aortic valve implantation (TAVI). However, delirium after transcatheter aortic valve implantation (DAT) is difficult to predict and its pathophysiology is still unclear. We aimed to investigate whether preoperative cerebral blood flow (CBF) is associated with DAT and, if so, whether CBF measurement is useful for predicting DAT. Methods We evaluated CBF in 50 consecutive patients before TAVI (84.7±4.5 yrs., 36 females) using 99mTc ethyl cyst

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Marques, Esteban A., Stefan De Gendt, Geoffrey Pourtois, and Michiel J. van Setten. "Benchmarking First-Principles Reaction Equilibrium Composition Prediction." Molecules 28, no. 9 (2023): 3649. http://dx.doi.org/10.3390/molecules28093649.

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The availability of thermochemical properties allows for the prediction of the equilibrium compositions of chemical reactions. The accurate prediction of these can be crucial for the design of new chemical synthesis routes. However, for new processes, these data are generally not completely available. A solution is the use of thermochemistry calculated from first-principles methods such as Density Functional Theory (DFT). Before this can be used reliably, it needs to be systematically benchmarked. Although various studies have examined the accuracy of DFT from an energetic point of view, few s

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de Carvalho, Isabella Marques, Yhan da Silva Mutz, Amanda Cristina Gomes Machado, Amanda Aparecida de Lima Santos, Elisângela Jaqueline Magalhães, and Cleiton Antônio Nunes. "Exploring Strategies to Mitigate the Lightness Effect on the Prediction of Soybean Oil Content in Blends of Olive and Avocado Oil Using Smartphone Digital Image Colorimetry." Foods 12, no. 18 (2023): 3436. http://dx.doi.org/10.3390/foods12183436.

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Abstract (sommario):

Extra virgin olive oil (EVOO) and avocado oil (AVO) are recognized for their unique sensory characteristics and bioactive compounds. Declared blends with other vegetable oils are legal, but undeclared mixing is a common type of fraud that can affect product quality and commercialization. In this sense, this study explored strategies to mitigate the influence of lighting in order to make digital image colorimetry (DIC) using a smartphone more robust and reliable for predicting the soybean oil content in EVOO and AVO blends. Calibration models were obtained by multiple linear regression using th

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Kozhemiakin, Ruslan, Oleksandr Zemliachenko, Volodymyr Lukin, Sergii Abramov, and Benoit Vozel. "An approach to prediction and providing of compression ratio for DCT based coder applied to remote sensing images." Ukrainian journal of remote sensing, no. 9 (June 29, 2016): 22–29. http://dx.doi.org/10.36023/ujrs.2016.9.67.

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Abstract (sommario):

A novel compression ratio prediction and providing technique applicable to noisy and almost noise-free remote sensing images is proposed. It allows predicting and then providing a desired compression ratio for DCT-based coder in automatically manner. The proposed technique is algorithmically simple and has low computational complexity that allows using it onboard spaceborne or airborne carriers. The study is carried out for test and real-life Hyperion images. It is shown that the proposed technique has high accuracy and it is robust with respect to noise intensity and type. Relative error of p

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Maček, Andraž, Bojan Starman, Sam Coppieters, Janez Urevc, and Miroslav Halilovič. "Confidence intervals of inversely identified material model parameters: A novel two-stage error propagation model based on stereo DIC system uncertainty." Optics and Lasers in Engineering 174 (June 5, 2024): 107958. https://doi.org/10.1016/j.optlaseng.2023.107958.

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Abstract (sommario):

Digital image correlation (DIC) is a powerful tool for characterising materials and determining material model parameters. To assess the reliability of the full-field measurement-based inverse identification procedures, it is crucial to investigate the impact of the measurement errors on the identified material model parameters. Literature indicates that conventional error propagation models, which rely on Gaussian noise-contaminated data, significantly overestimate the confidence for inversely identified material model parameters, resulting in misleadingly narrow confidence intervals. A more

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Gaumard, Robin, Dominik Dragún, Jesús N. Pedroza-Montero, et al. "Regression Machine Learning Models Used to Predict DFT-Computed NMR Parameters of Zeolites." Computation 10, no. 5 (2022): 74. http://dx.doi.org/10.3390/computation10050074.

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Abstract (sommario):

Machine learning approaches can drastically decrease the computational time for the predictions of spectroscopic properties in materials, while preserving the quality of the computational approaches. We studied the performance of kernel-ridge regression (KRR) and gradient boosting regressor (GBR) models trained on the isotropic shielding values, computed with density-functional theory (DFT), in a series of different known zeolites containing out-of-frame metal cations or fluorine anion and organic structure-directing cations. The smooth overlap of atomic position descriptors were computed from

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Lu, Chengqiang, Qi Liu, Chao Wang, Zhenya Huang, Peize Lin, and Lixin He. "Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1052–60. http://dx.doi.org/10.1609/aaai.v33i01.33011052.

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Abstract (sommario):

Predicting molecular properties (e.g., atomization energy) is an essential issue in quantum chemistry, which could speed up much research progress, such as drug designing and substance discovery. Traditional studies based on density functional theory (DFT) in physics are proved to be time-consuming for predicting large number of molecules. Recently, the machine learning methods, which consider much rule-based information, have also shown potentials for this issue. However, the complex inherent quantum interactions of molecules are still largely underexplored by existing solutions. In this pape

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Watabe, Takafumi, Yukinari Muramatsu, Masaru Homma, Tadahisa Higashide, and Dong-Hyuk Ahn. "Development of a Simple Empirical Yield Predition Model Based on Dry Matter Production in Sweet Pepper." Agriculture (Pol'nohospodárstvo) 68, no. 1 (2022): 13–24. http://dx.doi.org/10.2478/agri-2022-0002.

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Abstract (sommario):

Abstract The development of models for yield prediction in greenhouse sweet peppers may help improve yield and labour productivity. We aimed to monitor the growth and yield of hydroponically grown sweet pepper plants without destructive sampling. First, we constructed a prediction model and validated it in a cultivation experiment. In the developed model, daily node appearance and light use efficiency were predicted from daily mean air temperature and daytime carbon dioxide (CO2) concentration. The daily light interception was obtained by non-destructive leaf area estimation. Second, we valida

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Kunwar, Deepak. "Health Care Disease Prediction and Medicine, Exercise and Diet Suggestion using CNN." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 766–72. http://dx.doi.org/10.22214/ijraset.2021.36413.

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Abstract (sommario):

The Disease prediction Program is based on a prediction model predicting user disease on the basis of the following indicators the user contributes as an input to the system.. The system analyzes the symptoms provided by the user as input and gives the probability of the disease as an output Disease Prediction is done by implementing the CNN Classifier. CNN Classifier calculates the probability of the disease. Along with disease prediction system also calculates severity of disease and as per severity of disease suggests medicine. Suggesting diet and appropriate exercise is another merit of pr

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Chintalapati, Krishnam Raju, Yesudas Kada, Vasavi Malkhed, Sanath Kumar Goud Palusa, Rabin Bera, and V. Shanmukha Kumar Jagarlapudi. "In silico Studies of Cilnidipine Degradation Products for Structure Confirmation, Toxicity Prediction and Molecular Docking." Asian Journal of Chemistry 36, no. 4 (2024): 865–78. http://dx.doi.org/10.14233/ajchem.2024.31150.

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Abstract (sommario):

In this study, a comprehensive analysis of cilnidipine and its degradation products (KD1-KD4 and CD1-CD3) with three main objectives viz. (i) toxicity prediction for bacterial mutagenicity, (ii) assessment of pharmacological activity and (iii) density functional theory (DFT) calculations were performed for structure confirmation. For bacterial mutagenicity prediction, in silico assessments were performed following ICH M7 guidelines. Using rule-based and statistical-based methodologies, predictions revealed an alerting group in CD1-CD3, while no alerting group was observed in KD1-KD4 for bacter

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Ouyang, Wei Ping, Jian Ping Lin, and Zhi Guo Lu. "Research of Stress Transfer Area and its Length Prediction of Single-Lap Adhesive Joint." Advanced Materials Research 129-131 (August 2010): 680–85. http://dx.doi.org/10.4028/www.scientific.net/amr.129-131.680.

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Abstract (sommario):

obtaining the law of stress and strain distribution of loaded adhesive joint has significant implication for joint design and its strength prediction. The dynamic FEM model of uniaxial tensile adhesive joint was established, in which strain fracture criteria is adopted. It can be observed from the FEM results that: lapped area of the joint bears shear stress primarily, the adherend areas located away from the lapped area bear steady tensile stress mainly and the adherend areas adjacent to lapped area endure tensile and shear stress simultaneously. Based on stress distribution characters, the j

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Zhao, Ziqi, Yonghong Xu, and Yong Zhao. "SXGBsite: Prediction of Protein–Ligand Binding Sites Using Sequence Information and Extreme Gradient Boosting." Genes 10, no. 12 (2019): 965. http://dx.doi.org/10.3390/genes10120965.

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Abstract (sommario):

The prediction of protein–ligand binding sites is important in drug discovery and drug design. Protein–ligand binding site prediction computational methods are inexpensive and fast compared with experimental methods. This paper proposes a new computational method, SXGBsite, which includes the synthetic minority over-sampling technique (SMOTE) and the Extreme Gradient Boosting (XGBoost). SXGBsite uses the position-specific scoring matrix discrete cosine transform (PSSM-DCT) and predicted solvent accessibility (PSA) to extract features containing sequence information. A new balanced dataset was

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Ibrahim, M. Arefeeen, Ajmeri Nusrat Shoma, and Saiful Hasan Tariq. "Predictions for ‘Purbachal’: Learning from ‘Dhanmondi’." AIUB Journal of Science and Engineering (AJSE) 16, no. 1 (2017): 11–18. http://dx.doi.org/10.53799/ajse.v16i1.27.

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Abstract (sommario):

Township planning was introduced from early ‘50s in Dhaka, the capital city of Bangladesh having a current population of 14 million approximately. To meet the demand of growing number of population, Dhaka has witnessed different new township projects from the ‘60s to ‘90s. Example of some of these similar developments by government, includes Dhanmondi, Banani, Gulshan, Uttara, Baridhara etc. Hence, old Dhaka city is expanding its civic facilities by urbanizing in the vicinity of city. Under this scenario, a new township project, Purbachal New Town, was planned by concerned government organizat

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Timrov, Iurii. "(Invited) Extended Hubbard Functionals for Accurate Modeling of Li-Ion Battery Cathode Materials." ECS Meeting Abstracts MA2024-01, no. 23 (2024): 1383. http://dx.doi.org/10.1149/ma2024-01231383mtgabs.

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Abstract (sommario):

Designing novel cathode materials for Li-ion batteries necessitates accurate first-principles predictions of their properties. Density-functional theory (DFT) employing standard (semi-)local functionals encounters challenges due to pronounced self-interaction errors within the partially filled d shells of transition-metal (TM) elements. Here, we demonstrate the efficacy of DFT with extended Hubbard functionals in accurately predicting the "digital" change in oxidation states of TM ions for mixed-valence phases at intermediate Li concentrations in phospho-olivine and spinel cathode materials. T

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Shi, Benyun, Conghui Ge, Hongwang Lin, et al. "Sea Surface Temperature Prediction Using ConvLSTM-Based Model with Deformable Attention." Remote Sensing 16, no. 22 (2024): 4126. http://dx.doi.org/10.3390/rs16224126.

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Abstract (sommario):

Sea surface temperature (SST) prediction has received increasing attention in recent years due to its paramount importance in the various fields of oceanography. Existing studies have shown that neural networks are particularly effective in making accurate SST predictions by efficiently capturing spatiotemporal dependencies in SST data. Among various models, the ConvLSTM framework is notably prominent. This model skillfully combines convolutional neural networks (CNNs) with recurrent neural networks (RNNs), enabling it to simultaneously capture spatiotemporal dependencies within a single compu

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Qin Cheng-Long, Zhao Liang, and Jiang Gang. "Prediction of thermodynamic stability of rare earth compounds by machine learning model." Acta Physica Sinica 74, no. 13 (2025): 0. https://doi.org/10.7498/aps.74.20250362.

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Abstract (sommario):

The research aims to predict the thermodynamic stability of rare-earth compounds using machine learning (ML) models, providing crucial data support for advanced materials design and facilitating the discovery of new rare-earth compounds. </p> <p>In terms of methods, this study is based on a dataset consisting of 280,569 compounds. The formation energies of these compounds were obtained through density functional theory (DFT) calculations. A system of 145 feature descriptors was constructed, covering stoichiometric properties, statistical properties of elements, electronic structure

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Garg, Rishabh. "Blockchain foar Real World Applications." December 31, 2022. https://doi.org/10.5281/zenodo.7466403.

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Abstract (sommario):

Wy binne yn 'e midden fan in oare grutte revolúsje neamd BLOCKCHAIN, in ferspraat databank dy't ûnderhâldt in hieltyd groeiende list fan records, neamd blokken. Dit ynnovaasjelânskip fertsjintwurdiget mar 12 jier wurk troch in elite groep geeks, kryptografen en wiskundigen. Yn 'e kommende tiid sil blockchain elke minsklike efterfolging permeate, wêrtroch prosessen effisjint en tûk wurde. As it folsleine potensjeel fan dizze trochbraken yn 'e maatskippij realisearre wurdt, sille dingen stadichoan oars begjinne te barren - ynternasjonale jildt

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Garg, Rishabh. "Blockchain voor toepassingen in de echte wereld." January 1, 2023. https://doi.org/10.5281/zenodo.7467086.

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Abstract (sommario):

We zitten midden in een andere grote revolutie genaamd BLOCKCHAIN, een gedistribueerde database die een steeds groter wordende lijst met records bijhoudt, blokken genaamd. Dit innovatielandschap vertegenwoordigt slechts 12 jaar werk door een elitegroep van nerds, cryptografen en wiskundigen. In de toekomst zal blockchain elk menselijk streven doordringen, waardoor processen efficiënt en slim worden. Naarmate het volledige potentieel van deze doorbraken in de samenleving wordt gerealiseerd, zullen dingen geleidelijk anders gaan gebeuren – internationale geldovermakingen zullen snelle

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